"""simple docstring"""



import random
from typing import Any



def 	_lowerCamelCase(	a				):
       for _ in range(len(a				)				):
              __a       =    random.randint(0   ,    len(a				) - 1				)
              __a       =    random.randint(0   ,    len(a				) - 1				)
              __a ,   __a       =    data[b], data[a]
       return data


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Union[str, Any]									=			[0, 1, 2, 3, 4, 5, 6, 7]
   SCREAMING_SNAKE_CASE__:str									=			["""python""", """says""", """hello""", """!"""]
   print("""Fisher-Yates Shuffle:""")
   print("""List""", integers, strings)
   print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))

"""simple docstring"""



import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


SCREAMING_SNAKE_CASE__:Optional[Any]									=			logging.get_logger(__name__)






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		lowerCamelCase						):
        super().__init__()
        __a       =    nn.ModuleList(lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = False    ,		lowerCamelCase = True    ,		):
        for i, (image, scale, controlnet) in enumerate(zip(lowerCamelCase    ,		lowerCamelCase    ,		self.nets						)						):
               __a ,   __a       =    controlnet(
                   lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		)

               # merge samples
               if i == 0:
                      __a ,   __a       =    down_samples, mid_sample
               else:
                      __a       =    [
                          samples_prev + samples_curr
                          for samples_prev, samples_curr in zip(lowerCamelCase    ,		lowerCamelCase						)
                      ]
                      mid_block_res_sample += mid_sample

        return down_block_res_samples, mid_block_res_sample


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = False    ,		lowerCamelCase = None    ,		):
        __a       =    0
        __a       =    save_directory
        for controlnet in self.nets:
               controlnet.save_pretrained(
                   lowerCamelCase    ,		is_main_process=lowerCamelCase    ,		save_function=lowerCamelCase    ,		safe_serialization=lowerCamelCase    ,		variant=lowerCamelCase    ,		)

               idx += 1
               __a       =    model_path_to_save + F"_{idx}"






 @classmethod
 def 						a__   ( cls    ,		lowerCamelCase    ,		**lowerCamelCase						):
        __a       =    0
        __a       =    []

        # load controlnet and append to list until no controlnet directory exists anymore
        # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
        # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
        __a       =    pretrained_model_path
        while os.path.isdir(lowerCamelCase						):
               __a       =    ControlNetModel.from_pretrained(lowerCamelCase    ,		**lowerCamelCase						)
               controlnets.append(lowerCamelCase						)

               idx += 1
               __a       =    pretrained_model_path + F"_{idx}"

        logger.info(F"{len(lowerCamelCase						)} controlnets loaded from {pretrained_model_path}."						)

        if len(lowerCamelCase						) == 0:
               raise ValueError(
                   F"No ControlNets found under {os.path.dirname(lowerCamelCase						)}. Expected at least {pretrained_model_path + '_0'}."						)

        return cls(lowerCamelCase						)
